Book Image

Python Deep Learning Projects

By : Matthew Lamons, Rahul Kumar, Abhishek Nagaraja

Book Image

Python Deep Learning Projects

By: Matthew Lamons, Rahul Kumar, Abhishek Nagaraja

Overview of this book

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Building Deep Learning Environments

Building Deep Learning Environments

Building a common DL environment

DL environment setup locally

Setting up a DL environment in the cloud

Cloud platforms for deployment

Automating the setup process

Training NN for Prediction Using Regression

Training NN for Prediction Using Regression

Building a regression model for prediction using an MLP deep neural network

Exploring the MNIST dataset

Intuition and preparation

Let's code the implementation!

Concluding the project

Word Representation Using word2vec

Word Representation Using word2vec

Learning word vectors

Visualizing the embedding space by plotting the model on TensorBoard

Building language models using CNN and word2vec

Building an NLP Pipeline for Building Chatbots

Building an NLP Pipeline for Building Chatbots

Basics of NLP pipelines

Building conversational bots

Serving chatbots

Sequence-to-Sequence Models for Building Chatbots

Sequence-to-Sequence Models for Building Chatbots

Introducing RNNs

Implementing basic RNNs

LSTM architecture

Implementing an LSTM model

Sequence-to-sequence models

Generative Language Model for Content Creation

Generative Language Model for Content Creation

LSTM for text generation

Generating lyrics using deep (multi-layer) LSTM

Generating music using a multi-layer LSTM

Building Speech Recognition with DeepSpeech2

Building Speech Recognition with DeepSpeech2

Data preprocessing

DS2 model description and intuition

Training the model

Testing and evaluating the model

Handwritten Digits Classification Using ConvNets

Handwritten Digits Classification Using ConvNets

Code implementation

Object Detection Using OpenCV and TensorFlow

Object Detection Using OpenCV and TensorFlow

Object detection intuition

Object detection using OpenCV

Object detection using deep learning

Image segmentation

Building Face Recognition Using FaceNet

Building Face Recognition Using FaceNet

Setup environment

Building the pipeline

Preprocessing of images

Training the classifier

Automated Image Captioning

Automated Image Captioning

Data preparation

Defining the captioning model

Training the captioning model

Evaluating the captioning model

Deploying the captioning model

Pose Estimation on 3D models Using ConvNets

Pose Estimation on 3D models Using ConvNets

Code implementation

Importing the dependencies

Image Translation Using GANs for Style Transfer

Image Translation Using GANs for Style Transfer

Let's code the implementation!

The conclusion to the project

Develop an Autonomous Agent with Deep R Learning

Develop an Autonomous Agent with Deep R Learning

Let's get to the code!

Deep Q-learning

Deep SARSA learning

The conclusion to the project

Summary and Next Steps in Your Deep Learning Career

Summary and Next Steps in Your Deep Learning Career

Python deep learning – building the foundation – two projects

Python deep learning – NLP – 5 projects

Deep learning – computer vision – 6 projects

Python deep learning – autonomous agents – 1 project

Next steps – AI strategy and platforms

Conclusion and thank you!

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Data preprocessing

In this project, we will use LibriSpeech ASR corpus (http://www.openslr.org/12/), which is 1,000 hours of 16 kHz-read English speech.

Let's use the following commands to download the corpus and unpack the LibriSpeech data:

mkdir -p data/librispeech
cd data/librispeech
wget http://www.openslr.org/resources/12/train-clean-100.tar.gz
wget http://www.openslr.org/resources/12/dev-clean.tar.gz
wget http://www.openslr.org/resources/12/test-clean.tar.gz
mkdir audio
cd audio
tar xvzf ../train-clean-100.tar.gz LibriSpeech/train-clean-100 --strip-components=1
tar xvzf ../dev-clean.tar.gz LibriSpeech/dev-clean --strip-components=1
tar xvzf ../test-clean.tar.gz LibriSpeech/test-clean --strip-components=1

This will take a while and once the process is completed, we will have the data folder structure, as shown in the following screenshot:

We now have three folders named as train...